This study analyses the efficiency in knowledge transmission of organizations and local regions participating in European R&D projects in 2000–2013 within renewable energy (RE) sectors (wind, solar, sea, geothermal, and biomass) using social network analysis (SNA). A review of the collaborative R&D consortium networks as technological transfer structures and public policy support issues was carried out. Then, not only is the traditional SNA centrality perspective of actors employed to identify key players who bridge less connected areas, but also the structural hole approach is applied based on the relative position, role, and potential redundancy of collaborations from the overall network perspective. It reveals that networks of organizations and local regions are neither completely random nor homogenous in terms of cohesion and efficiency. Additionally, the existence of areas between core and peripheral nodes (structural holes) is confirmed. Higher education and research centers, which show a greater influential position and higher experience, take advantage of them. Research concludes that the efficiency in terms of knowledge transmission is not always positively correlated with high centrality values. The most emergent RE sectors still appear less efficient according to the rankings produced integrating both approaches. This paper makes a novel academic contribution to RE policy makers since a new application of centrality and efficiency perspectives is applied. As a result, policy makers should consider it in detail when designing public RE projects with the aim of building an efficient European Research Area.

1.
M.
Pacesila
,
S. G.
Burcea
, and
S. E.
Colesca
, “
Analysis of renewable energies in European Union
,”
Renewable Sustainable Energy Rev.
56
,
156
170
(
2016
).
2.
G.
San Miguel
,
P.
del Río
, and
F.
Hernández
, “
An update of Spanish renewable energy policy and achievements in a low carbon context
,”
J. Renewable Sustainable Energy
2
,
31007
(
2010
).
3.
A. C.
Marques
and
J. A.
Fuinhas
, “
Is renewable energy effective in promoting growth?
,”
Energy Policy
46
,
434
442
(
2012
).
4.
European Commission
,
Towards a European Research Area: Communication from the Commission to the Council, the European Parliament, the Economic and Social Committee and the Committee of the Regions
(
NAi Publishers
,
2007
).
5.
T.
Luukkonen
and
M.
Nedeva
, “
Towards understanding integration in research and research policy
,”
Res. Policy
39
,
674
686
(
2010
).
6.
B.
Lepori
,
E.
Reale
, and
P.
Larédo
, “
Logics of integration and actors' strategies in European joint programs
,”
Res. Policy
43
,
391
402
(
2014
).
7.
M.
Nedeva
, “
Between the global and the national: Organising European science
,”
Res. Policy
42
,
220
230
(
2013
).
8.
European Commission
,
Impact Assessment: Communication from the Commission Horizon 2020 - The framework programme for research and innovation
(
EU Publication Office
,
2011
), p.
112
.
9.
European Commission
, “
Renewable energy: A major player in the European energy market
,” in
Communication from Commisison to the Europian Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions
(
EU Publication Office
,
2012
), p.
2
.
10.
M.
da Graça Carvalho
, “
EU energy and climate change strategy
,”
Energy
40
,
19
22
(
2012
).
11.
M.
Giacomarra
and
F.
Bono
, “
European Union commitment towards RES market penetration: From the first legislative acts to the publication of the recent guidelines on State aid 2014/2020
,”
Renewable Sustainable Energy Rev.
47
,
218
232
(
2015
).
12.
L.
Kitzing
,
C.
Mitchell
, and
P. E.
Morthorst
, “
Renewable energy policies in Europe: Converging or diverging?
,”
Energy Policy
51
,
192
201
(
2012
).
13.
C.
Klessmann
,
A.
Held
,
M.
Rathmann
, and
M.
Ragwitz
, “
Status and perspectives of renewable energy policy and deployment in the European Union—What is needed to reach the 2020 targets?
,”
Energy Policy
39
,
7637
7657
(
2011
).
14.
International Renewable Energy Agency
,
International Standardisation in the Field of Renewable Energy
(
International Renewable Energy Agency (IRENA)
,
2013
), p.
74
.
15.
A.
Vantoch-Wood
and
P. M.
Connor
, “
Using network analysis to understand public policy for wave energy
,”
Energy Policy
62
,
676
685
(
2013
).
16.
M. I.
Blanco
and
G.
Rodrigues
, “
Direct employment in the wind energy sector: An EU study
,”
Energy Policy
37
,
2847
2857
(
2009
).
17.
M.
Sun
,
H.
Zhang
,
D.
Han
, and
A.
Gao
, “
A hierarchical-network-model based analysis of the market characteristics of China's photovoltaic enterprises
,”
J. Renewable Sustainable Energy
6
,
43113
(
2014
).
18.
N.
Nikodinoska
,
M.
Mattivi
,
S.
Notaro
, and
A.
Paletto
, “
Stakeholders' appraisal of biomass-based energy development at local scale
,”
J. Renewable Sustainable Energy
7
,
23117
(
2015
).
19.
M.
Matt
,
S.
Robin
, and
S.
Wolff
, “
The influence of public programs on inter-firm R&D collaboration strategies: Project-level evidence from EU FP5 and FP6
,”
J. Technol. Transfer
37
,
885
916
(
2012
).
20.
C.
Dimos
and
G.
Pugh
, “
The effectiveness of R&D subsidies: A meta-regression analysis of the evaluation literature
,”
Res. Policy
45
,
797
815
(
2016
).
21.
M.
Schwartz
,
F.
Peglow
,
M.
Fritsch
, and
J.
Günther
, “
What drives innovation output from subsidized R&D cooperation?—Project-level evidence from Germany
,”
Technovation
32
,
358
369
(
2012
).
22.
M.
Ragwitz
and
A.
Miola
, “
Evidence from RD&D spending for renewable energy sources in the EU
,”
Renewable Energy
30
,
1635
1647
(
2005
).
23.
S. M.
McCauley
and
J. C.
Stephens
, “
Green energy clusters and socio-technical transitions: Analysis of a sustainable energy cluster for regional economic development in Central Massachusetts, USA
,”
Sustainable Sci.
7
,
213
225
(
2012
).
24.
A. N.
Menegaki
, “
Growth and renewable energy in Europe: A random effect model with evidence for neutrality hypothesis
,”
Energy Econ.
33
,
257
263
(
2011
).
25.
D.
Helm
, “
The European framework for energy and climate policies
,”
Energy Policy
64
,
29
35
(
2014
).
26.
European Commission
,
Study on Network Analysis of the 7th Framework Programme Participation Final Report
(
EU Publication Office
,
2015
).
27.
F.
Hervás Soriano
and
F.
Mulatero
, “
EU research and innovation (R&I) in renewable energies: The role of the strategic energy technology plan (SET-Plan)
,”
Energy Policy
39
,
3582
3590
(
2011
).
28.
S. O.
Negro
,
F.
Alkemade
, and
M. P.
Hekkert
, “
Why does renewable energy diffuse so slowly? A review of innovation system problems
,”
Renewable Sustainable Energy Rev.
16
,
3836
3846
(
2012
).
29.
G.
Jaegersberg
and
J.
Ure
, “
Barriers to knowledge sharing and stakeholder alignment in solar energy clusters: Learning from other sectors and regions
,”
J. Strategic Inf. Syst.
20
,
343
354
(
2011
).
30.
E.
Michalena
and
J. M.
Hills
, “
Renewable energy issues and implementation of European energy policy: The missing generation?
,”
Energy Policy
45
,
201
216
(
2012
).
31.
A.
Aslani
, “
Strategic variables of commercialization of renewable energy technologies
,”
J. Renewable Sustainable Energy
7
,
23105
(
2015
).
32.
A.
Vazquez
,
S.
Astariz
, and
G.
Iglesias
, “
A strategic policy framework for promoting the marine energy sector in Spain
,”
J. Renewable Sustainable Energy
7
,
61702
(
2015
).
33.
J.
Hoekman
,
K.
Frenken
, and
F.
van Oort
, “
The geography of collaborative knowledge production in Europe
,”
Ann. Reg. Sci.
43
,
721
738
(
2008
).
34.
M. T.
Costa-Campi
,
N.
Duch-Brown
, and
J.
García-Quevedo
, “
R&D drivers and obstacles to innovation in the energy industry
,”
Energy Econ.
46
,
20
30
(
2014
).
35.
I.
Boie
,
C.
Fernandes
,
P.
Frías
, and
M.
Klobasa
, “
Efficient strategies for the integration of renewable energy into future energy infrastructures in Europe—An analysis based on transnational modeling and case studies for nine European regions
,”
Energy Policy
67
,
170
185
(
2014
).
36.
B. S.
Aharonson
and
M. A.
Schilling
, “
Mapping the technological landscape: Measuring technology distance, technological footprints, and technology evolution
,”
Res. Policy
45
,
81
96
(
2016
).
37.
H. Y.
Cho
,
J. H.
Kim
,
K. J.
Lee
,
S. H.
Lee
, and
N.
Park
, “
Network analysis of photovoltaic-related Science Citation Index papers in Korea
,”
J. Renewable Sustainable Energy
7
,
63127
(
2015
).
38.
N.
Step
,
M.
Thelwall
, and
D.
Stuart
,
The use of webometrics for the analysis of knowledge flows within the European Research Area
(
EU Publication Office
,
2009
), pp.
1
178
.
39.
N.
Arranz
and
J. C.
Fdez de Arroyabe
, “
The choice of partners in R&D cooperation: An empirical analysis of Spanish firms
,”
Technovation
28
,
88
100
(
2008
).
40.
A.
Darmani
,
N.
Arvidsson
,
A.
Hidalgo
, and
J.
Albors
, “
What drives the development of renewable energy technologies? Toward a typology for the systemic drivers
,”
Renewable Sustainable Energy Rev.
38
,
834
847
(
2014
).
41.
Y.
Geum
,
S.
Lee
,
B.
Yoon
, and
Y.
Park
, “
Identifying and evaluating strategic partners for collaborative R&D: Index-based approach using patents and publications
,”
Technovation
33
,
211
224
(
2013
).
42.
L. M.
Romo-Fernández
,
C.
López-Pujalte
,
V. P.
Guerrero Bote
, and
F.
Moya-Anegón
, “
Analysis of Europe's scientific production on renewable energies
,”
Renewable Energy
36
,
2529
2537
(
2011
).
43.
M. F.
Arroyabe
,
N.
Arranz
,
J. C. Fdez.
de Arroyabe
, and
J. C.
Juan
, “
R&D partnerships: An exploratory approach to the role of structural variables in joint project performance
,”
Technol. Forecasting Soc. Change
90
,
623
634
(
2015
).
44.
J. E.
Mote
, “
R&D ecology: Using 2-mode network analysis to explore complexity in R&D environments
,”
J. Eng. Technol. Manag.
22
,
93
111
(
2005
).
45.
Y.
Krogmann
,
N.
Riedle
, and
U.
Schwalbe
, “
Inter-firm R&D networks in pharmaceutical biotechnology: What determines Firm's centrality-based partnering capability
?,”
FZID Discussion Paper
(
Universität Hohenheim
,
2013
).
46.
X.
Zheng
,
Y.
Le
,
A. P. C.
Chan
,
Y.
Hu
, and
Y.
Li
, “
Review of the application of social network analysis (SNA) in construction project management research
,”
Int. J. Proj. Manag.
34
,
1214
1225
(
2016
).
47.
L.
Cassi
,
N.
Corrocher
,
F.
Malerba
, and
N.
Vonortas
, “
The impact of EU-funded research networks on knowledge diffusion at the regional level
,”
Res. Eval.
17
,
283
293
(
2008
).
48.
S.
Breschi
and
L.
Cusmano
, “
Unveiling the texture of a European research area: Emergence of oligarchic network under EU frameworks programmes
,”
Int. J. Technol.
27
(
8
),
747
772
(
2002
).
49.
T.
Roediger-Schluga
and
M. J.
Barber
, “
R&D collaboration networks in the European Framework Programmes: Data processing, network construction and selected results
,”
Int. J. Foresight Innovation Policy
4
,
321
(
2008
).
50.
M. J.
Barber
and
T.
Scherngell
, “
Inter-regional betweenness centrality in the European R & D network: Empirical investigation using European Framework data
,”
ERSA Conference Paper
(
2012
), pp.
21
25
.
51.
M. J.
Kang
and
J.
Park
, “
Analysis of the partnership network in the clean development mechanism
,”
Energy Policy
52
,
543
553
(
2013
).
52.
M. J.
Kang
and
J.
Hwang
, “
Structural dynamics of innovation networks funded by the European Union in the context of systemic innovation of the renewable energy sector
,”
Energy Policy
96
,
471
490
(
2016
).
53.
E.
Paulsson
, “
A review of the CDM literature: From fine-tuning to critical scrutiny?
,”
Int. Environ. Agreements: Political Law Econ.
9
,
63
80
(
2009
).
54.
N.
Arranz
and
J. C. Fdez.
de Arroyabe
, “
Can innovation network projects result in efficient performance?
,”
Technol. Forecasting Soc. Change
79
,
485
497
(
2012
).
55.
A.
Protogerou
,
Y.
Caloghirou
, and
E.
Siokas
,
The Nature of EU Funded R & D Collaboration Networks in the Area of Information Society Technologies
(
European Association for Evolutionary Political Economy (EAEPE)
,
2008
).
56.
B.
Heller-Schuh
 et al.,
Analysis of Networks in European Framework Programmes (1984-2006)
(
European Commission, Joint Research Centre, Institute for Prospective Technological Studies
,
2011
), pp.
1
138
.
57.
M. J.
Barber
,
M.
Paier
, and
T.
Scherngell
, “
Analyzing and modeling European R&D collaborations: Challenges and opportunities from a large social network
,” in
Analysis of Complex Networks: From Biology to Linguistics
(
Wiley
,
2009
), pp.
401
423
.
58.
I.
Wanzenböck
,
T.
Scherngell
, and
R.
Lata
, “
Embeddedness of European regions in European Union-funded research and development (R&D) networks: A spatial econometric perspective
,”
Reg. Stud.
49
,
1685
1705
(
2015
).
59.
B.
Verspagen
, “
Small worlds and technology networks
,” in
Knowledge Flows in European Industry
(
Routledge
,
2006
), Vol.
35
, Chap. 13, p.
299
.
60.
R.
Cowan
, “
Network models of innovation and knowledge diffusion
,” in
Clusters Networks and Innovations
(
Oxford University Press
,
2005
), pp.
29
53
.
61.
Copenhagen Cleantech Cluster
,
Quartz+CO and MEC Intelligence, The Global Cleantech Report 2012
(
Copenhagen Cleantech Cluster
,
2012
), p.
46
.
62.
J. S.
Coleman
, “
Social capital in the creation of human capital
,”
Am. J. Sociol.
94
,
S95
(
1988
).
63.
S. B.
Andrews
and
R. S.
Burt
, “
Structural holes: The social structure of competition
,”
Adm. Sci. Q.
40
,
355
(
1995
).
64.
L.
Cassi
,
N.
Corrocher
,
F.
Malerba
, and
N.
Vonortas
, “
Research networks as infrastructure for knowledge diffusion in European regions
,”
Econ. Innovation New Technol.
17
,
663
676
(
2008
).
65.
R. S.
Burt
, “
Industry performance and indirect access to structural holes
,” in
Advances in Strategic Management
, edited by
J. A. C.
Baum
and
T. J.
Rowley
(
Emerald Group Publishing Limited
,
2008
), Vol. 25, pp.
315
360
, ISBN: 978-0-7623-1442-3, eISBN: 978-1-84950-531-4.
66.
D. J.
Watts
and
S. H.
Strogatz
, “
Collective dynamics of ‘small world’ networks
,”
Nature
393
,
440
442
(
1998
).
67.
F.
Rychen
and
J.-B.
Zimmermann
,
Industrial Clusters and the Knowledge Based Economy: From open to distributed structures?
(
2009
).
68.
C. L.
Hung
, “
Social networks, technology ties, and gatekeeper functionality: Implications for the performance management of R&D projects
,”
Res. Policy
46
,
305
315
(
2017
).
69.
C.
Boari
and
F.
Riboldazzi
, “
How knowledge brokers emerge and evolve: The role of actors' behaviour
,”
Res. Policy
43
,
683
695
(
2014
).
70.
E.
Giuliani
and
M.
Bell
, “
The micro-determinants of meso-level learning and innovation evidence from a Chilean wine cluster
,”
Res. Policy
34
,
47
68
(
2005
).
71.
A.
Morrison
 et al.,
Gatekeepers of Knowledge within Industrial Districts: Who They are, How they Interact
(
Universit{à} Commerciale Luigi Bocconi
,
2004
).
72.
A.
Hargadon
and
R. I.
Sutton
, “
Technology brokering and innovation in a product development firm
,”
Adm. Sci. Q.
42
,
716
749
(
1997
).
73.
R. S.
Burt
,
Structural holes: The Social Structure of Competition
(
Harvard University Press
,
Cambridge, Massachussetts
,
1992
), pp.
38
40
.
74.
J. A. C.
Baum
,
A. V.
Shipilov
, and
T. J.
Rowley
, “
Where do small worlds come from?
,”
Ind. Corporate Change
12
,
697
725
(
2003
).
75.
F.
Hvelplund
, “
Renewable energy and the need for local energy markets
,”
Energy
31
,
1957
1966
(
2006
).
76.
G.
Garechana
,
R.
Rio
,
E.
Cilleruelo
, and
J.
Gavilanes
, “
Tracking the evolution of waste recycling research using overlay maps of science
,”
Waste Manag.
32
,
1069
1074
(
2012
).
77.
D.
Rotolo
,
D.
Hicks
, and
B. R.
Martin
, “
What is an emerging technology?
,”
Res. Policy
44
,
1827
1843
(
2015
).
78.
S.
Arora
,
A.
Porter
,
J.
Youtie
, and
P.
Shapira
, “
Capturing new developments in an emerging technology: An updated search strategy for identifying nanotechnology research outputs
,”
Scientometrics
95
,
351
370
(
2013
).
79.
M. E.
Porter
,
J.
Kwek
,
C. O.
Neil
,
A.
Satchcrof
, and
T.
Vogt
,
The Australian Renewable Energy Cluster—Microeconomics of Competitivenes
(
Australian Renewable Energy Cluster
,
2008
).
80.
F.
Rizzi
,
N. J.
van Eck
, and
M.
Frey
, “
The production of scientific knowledge on renewable energies: Worldwide trends, dynamics and challenges and implications for management
,”
Renewable Energy
62
,
657
671
(
2014
).
81.
S. W.
Cunningham
,
A. L.
Porter
, and
N. C.
Newman
, “
Special issue on tech mining
,”
Technol. Forecasting Soc. Change
73
,
915
922
(
2006
).
82.
Metaweb Technologies, I. G. OpenRefine,
2010
.
83.
Eurostat
,
Regions in the European Union: Nomenclature of territorial units for statistics NUTS 2013/EU-28 Statistics
(
Eurostat, European Commission
,
2015
).
84.
GPSVisualizer [Computer software]
. (2004). Retrieved from http://www.gpsvisualizer.com/ in 10 December
2016
.
85.
V.
Batagelj
and
A.
Mrvar
,
Pajek: Program for Analysis and Visualization of Large Networks, Reference Manual
(
University of Ljubljana
,
2011
), p.
96
.
86.
S. P.
Borgatti
,
M. G.
Everett
, and
L. C.
Freeman
,
Ucinet for Windows: Software for Social Network Analysis
(
Analytic Technologies
,
Harvard, MA
,
2002
).
87.
N. J.
van Eck
and
L.
Waltman
, “
Software survey: VOSviewer, a computer program for bibliometric mapping
,”
Scientometrics
84
,
523
538
(
2010
).
88.
National Center for Visual Analytics (NCVA)
,
Europe eXplorer
(
Linköping University
,
2008
).
89.
T. W.
Valente
and
K.
Fujimoto
, “
Bridging: Locating critical connectors in a network
,”
Soc. Networks
32
,
212
220
(
2010
).
90.
H.
Choe
,
D. H.
Lee
,
H. D.
Kim
, and
I. W.
Seo
, “
Structural properties and inter-organizational knowledge flows of patent citation network: The case of organic solar cells
,”
Renewable Sustainable Energy Rev.
55
,
361
370
(
2016
).
91.
J.
Guan
and
N.
Liu
, “
Invention profiles and uneven growth in the field of emerging nano-energy
,”
Energy Policy
76
,
146
157
(
2015
).
92.
F. G.
Montoya
,
M. G.
Montoya
,
J.
Gómez
,
F.
Manzano-Agugliaro
, and
E.
Alameda-Hernández
, “
The research on energy in Spain: A scientometric approach
,”
Renewable Sustainable Energy Rev.
29
,
173
183
(
2014
).
93.
M. E. J.
Newman
,
S. H.
Strogatz
, and
D. J.
Watts
, “
Random graphs with arbitrary degree distributions and their applications
,”
Phys. Rev. E
64
,
26118
(
2001
).
94.
V.
Batagelj
and
A.
Mrvar
, “
Pajek - Analysis and visualization of large networks
,” in
Lecture Notes in Computer Science
(
2002
), Vol. 2265, p.
477
.
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